Detect patterns in sequences of actions
This task falls within the overlapping fields of information extraction and pattern mining. Information extraction involves automatically extracting instances of specified relations from data. While pattern mining involves using data mining algorithms to discover interesting, unexpected and useful patterns between data in databases (Philippe F).

On your question you have stated that you have experimented with markov models with poor results. A better approach if you prefer working with markov models would be to use hierarchical markov models. Hierarchical markov models have multiple 'levels' of states which can describe input sequences at different levels of granularity. Hierarchical markov models are good at categorizing human behavior at various levels of abstraction i.e. a persons location in a room can be further interpreted to determine more complex information such as what activity the person is performing.  

However my recommendation is that you implement random forest classifiers for this problem. Random forests provide excellent classification accuracy with a relatively simple implementation. Additionally random forests provide the ability to inspect trees for tweaking parameters to improve accuracy. You can also use cross validation to evaluate your model and calculate its accuracy. Consider using Python's Scikit-learn library's implementation of random forest classifier for this analysis. 

In your json code action package you have declared metrics such as DRAG_START, TRASH_SYMBOL, OPEN and CLOSE. My suggestion is that for your model to be accurate you also need to declare lower level actions such as: time between clicks, change in the direction of mouse motion, screen region hover count, task completion time and time between a click and a succeeding mouse movement. 

For further reference I recommend that you look at the papers below which I found useful and relevant to your question.

Hierarchical Hidden Markov Models for Information Extraction https://www.biostat.wisc.edu/~craven/papers/ijcai03.pdf

Detecting Abnormal User Behavior Through Pattern-mining Input Device Analytics https://www.ignacioxd.com/files/bib/Dominguez2015-Concentration.pdf

Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia   https://arxiv.org/ftp/arxiv/papers/1111/1111.1817.pdf